Estimation of transit demand models for enhancing ridership

ABSTRACT

A method of estimating a transit demand graph includes collecting conditional information that includes at least one condition that when satisfied converts at least one non-rider into a rider, generating a non-rider transit demand graph by satisfying one of the conditions, and generating a normalized transit demand graph from the non-rider transit demand graph and a rider transit demand graph. The riders use public transit and the non-riders do not use public transit. The non-rider transit demand graph shows the demand of the non-riders for a public transit route. The rider transit demand graph shows the demand of riders for the same public transit route.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Provisional Application No.13/153,725 filed on Jun. 6, 2011, the disclosure of which isincorporated by reference herein.

BACKGROUND

1. Technical Field

The present disclosure relates to transportation systems, and moreparticularly to estimation of transit demand models for enhancingridership of transportation systems.

2. Discussion of Related Art

Transportation (transit) systems such as urban bus or train systems havea multitude of scheduled routes, where each route is comprised of one ormore legs. The start and end points of each leg may be referred to as awaypoint. City planners may select the waypoints and how often atransportation vehicle is to stop at each waypoint based on manyfactors, such as knowledge of desirable destination sites (e.g., theMall, the Hospital, the Train Station, the University, etc.), highpopulation centers, and work schedules. For example, it is generally agood idea to have transportation vehicles stop at certain waypoints morefrequently between 9 am-5 pm since people typically work during thosetimes and position them near high population centers. However, it can bedifficult to optimally select the location of the waypoints and thefrequency at which a transportation vehicle should stop at eachwaypoint.

BRIEF SUMMARY

According to an exemplary embodiment of the present invention, a methodof estimating a transit demand graph includes collecting conditionalinformation that includes at least one condition, that when satisfied,converts at least one non-rider into a rider, generating a non-ridertransit demand graph by satisfying one of the conditions, and generatinga normalized transit demand graph from the non-rider transit demandgraph and a rider transit demand graph. The riders use public transitand the non-riders do not use public transit. The non-rider transitdemand graph shows the demand of the non-riders for a public transitroute. The rider transit demand graph shows the demand of riders for thesame public transit route. The method or portions thereof may beperformed by a data processing machine (e.g., a computer, processor,etc.).

According to an exemplary embodiment of the present invention, a methodof estimating a transit demand graph includes generating a non-ridertransit demand graph from positions of non-riders, positions ofwaypoints of public transit routes, and a condition that directs atleast one of the non-riders to become a rider of a public transit route,and generating a transit demand graph from the non-rider transit demandgraph and a rider transit demand graph. The non-rider transit demandgraph shows the demand of non-riders for the public transit route andthe rider transit demand graph shows the demand of riders for the samepublic transit route. The method may be performed by a data processingmachine (e.g., a computer, a processor, etc.).

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Exemplary embodiments of the invention can be understood in more detailfrom the following descriptions taken in conjunction with theaccompanying drawings in which:

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 4A and FIG. B show examples of transit demand graphs for a transitvehicle travelling along a single leg of a transit route.

FIG. 5 illustrates a method of estimating a transit demand graph thatmay be performed according to an exemplary embodiment of the invention.

FIG. 6A illustrates an example of a rider transit demand graph that maybe generated by the method of FIG. 5.

FIG. 6B illustrates an example of a non-rider transit demand graph thatmay be generated by the method of FIG. 5.

FIG. 6C illustrates a normalized transit demand graph that may begenerated from the transit demand graphs of FIGS. 6A and 6B based on themethod of FIG. 5

FIG. 7A illustrates an example of a rider transit demand graph for aroute that may have been updated by the method of FIG. 5.

FIG. 7B illustrates an example of a transit demand graph that may begenerated from FIGS. 6B and 7A by the method of FIG. 5.

FIGS. 8A and 8B illustrate examples transit demand graphs that may begenerated based on satisfying a condition according to the method ofFIG. 5.

DETAILED DESCRIPTION

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementations of theteachings recited herein are not limited to a cloud computingenvironment. Rather, embodiments of the present invention are capable ofbeing implemented in conjunction with any other type of computingenvironment now known or later developed.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone/smartphones 54A, desktop computer 54B, laptopcomputer 54C, GPS systems (e.g., carried by individuals, or locatedwithin trains, buses, taxis, etc.) 54D, Road Sensors 54E, RadioFrequency Identity (RFID) Systems 54F, and/or automobile computer system54N may communicate. Nodes 10 may communicate with one another. They maybe grouped (not shown) physically or virtually, in one or more networks,such as Private, Community, Public, or Hybrid clouds as describedhereinabove, or a combination thereof. This allows cloud computingenvironment 50 to offer infrastructure, platforms and/or software asservices for which a cloud consumer does not need to maintain resourceson a local computing device. It is understood that the types ofcomputing devices 54A-N shown in FIG. 2 are intended to be illustrativeonly and that computing nodes 10 and cloud computing environment 50 cancommunicate with any type of computerized device over any type ofnetwork and/or network addressable connection (e.g., using a webbrowser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere®application server software; and database software 70, in one exampleIBM DB2® database software. (IBM, zSeries, pSeries, xSeries,BladeCenter, WebSphere, and DB2 are trademarks of International BusinessMachines Corporation registered in many jurisdictions worldwide).

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and in particular, transit analytics 68 to generate transitdemand graphs.

A transit demand graph visualizes the demand for one or more publictransportation routes by people. Public transportation vehicles such asbuses, trains, subway-trains, ferries, etc. transport people acrossthese routes. A transit demand graph could be for example, the part orthe entire route of a single bus, where each leg is marked with theexpected passenger demand.

FIGS. 4A and 4B show examples of transit demand graph for a transitvehicle travelling along a single leg of a transit route (e.g., a busroute). FIG. 4A illustrates a leg A with a stop S1 at location 1 and astop S2 at location 2. The total demand T_(D) for the leg A is 30passengers out of a total capacity T_(C) of 60. For example, on average,a transit vehicle following leg A can expect to deliver 30 passengersfrom stop S1 to stop S2, even though the vehicle has room for 60passengers. FIG. 4B illustrates a modification of leg A, where stop S1has been moved to location 3, resulting in leg B. The total demand T_(D)for leg B is 40 passengers out of a total capacity T_(C) of 60. In thisexample, it is assumed that the modification was made based solely oninformation about riders. A rider is a person that takes publictransportation. For example, it is assumed that there was knowledge thatlocation 3 is closer to more existing riders than location 1.

However, the transit analytics 68 can estimate transit demand graphsbased on rider information and non-rider information. A non-rider is aperson that does not currently use public transportation, but could ifcertain conditions are right. The rider information may include thecurrent location of the riders and corresponding conditional informationabout what could make the riders become non-riders. The non-riderinformation may include the current location of the non-riders andcorresponding conditional information about what could make thenon-riders become riders of public transportation.

FIG. 5 illustrates a method of estimating a transit demand graph thatmay be performed by the transit analytics 68 according to an exemplaryembodiment of the invention. Referring to FIG. 5, the method includescollecting a rider transit demand graph (S501), collecting non-riderconditional information including a condition that converts a non-riderto a rider (S502), generating a non-rider transit demand graph bysatisfying the condition (S503), and generating a normalized transitdemand graph based on the rider transit demand graph and the non-ridertransit demand graph (S504). The collecting of the rider transit demandgraph can be performed after the non-rider conditional information iscollected or after the non-rider transit demand graph is generated.

FIG. 6A illustrates an example of a rider transit demand graph for aroute that may be collected by the method of FIG. 5. The route has arider demand R_(D) of 30. FIG. 6B illustrates an example of a non-ridertransit demand graph for the same route that may be collected by themethod of FIG. 5. For example, the conditional information gathered atblock S502 of the method may indicate that 50% of non-riders living nearstop S1 are likely to be come riders if the transit vehicle were toprovide WIFI service. At block S503 of the method the non-rider transitdemand graph of FIG. 6B may be generated by satisfying the conditionthat WIFI be present on the transit vehicle and applying this 50% ruleto the known number of non-riders living near stop S1 (e.g., assume ten)to arrive at the non-rider demand NR_(D) of 5. FIG. 6C illustrates anormalized transit demand graph that may be generated by the method ofFIG. 5 based on the rider transit demand graph of FIG. 6A and thenon-rider transit demand graph of FIG. 6B. For example, assuming theaddition of WIFI has not caused existing riders of the route to becomenon-riders, the normalized demand graph of FIG. 6C can be calculated bysumming the rider demand R_(D) of 30 with the non-rider demand NR_(D) of5 to arrive at a total transit demand T_(D) of 35.

In an alternate embodiment of the present invention, the method furtherincludes collecting rider conditional information including a conditionthat converts a rider to a non-rider (S505), updating the rider transitdemand graph by satisfying the rider condition (S506), and generating anormalized transit demand graph based on the updated rider transitdemand graph and the non-rider transit demand graph (S507).

FIG. 7A illustrates an example of a rider transit demand graph for theroute that may have been updated at block S506 in the method of FIG. 5.The rider demand R_(D) has been reduced from 30 to 20 at block S506where a rider condition collected at block S505 is satisfied indicatingthat some riders would be converted to non-riders. For example, assumethat the condition indicated that ⅓ of riders would become non-riders ifoverhead storage racks in the transportation vehicle were removed andthe addition of the WIFI required the removal of the racks. FIG. 7Billustrates the normalized transit demand graph, which may be generatedfrom block S507. The graph is a combination of the non-rider demandtransit graph in FIG. 6B and the rider demand transit graph in FIG. 7A,yielding a total transit demand of 25 (R_(D)=20+NR_(D)=5).

FIG. 8A illustrates an example of a normalized transit demand graphgenerated based on satisfying the condition that 50% of non-ridersliving within 2000 feet of a transit stop would become riders. Theexample assumes an existing rider demand R_(D) of 30 and a generatedNR_(D) of 5 due to the addition of stop S3 and the presence of 10non-riders within 2000 feet of stop S3. Accordingly, the total transitdemand T_(D) is 35. FIG. 8B illustrates an example of a normalizedtransit demand graph that was generated when another nearby route wasconsidered that includes stop S4. For example, the addition of stop S3created an additional rider demand R_(D) of 5 for the route since 5 ofthe riders that would normally take a transit vehicle that uses stop S4,have switched to a transit vehicle using stop S3. While a rider thatswitches from one route to another route is still a rider, they could beconsidered a non-rider of the original route.

The rider and non-rider demand graphs are based on the locations ofriders and non-riders and the location of each waypoint of each transitroute. For example, the locations of all the waypoints, the routeconfigurations, and the route types (e.g., bus route, subway route) mayhave been entered into a local database or a remote database, where eachis accessible to the transit analytics 68.

The locations of riders and non-riders can be deduced from sensor datainput to the transit analytics 68. These locations may include streetaddresses, GPS coordinates, etc. These locations and whether they belongto riders or non-riders can be inferred in various ways. For example,the locations can be inferred from the positions of mobile phones 54Aand their proximity or lack of proximity to known transit routes. Mobilephone tracking tracks the current position of a moving or stationarymobile phone. The phone can be located based at least on the roamingsignal it emits, which contacts nearby antenna towers, and does notrequire an active call. GSM localization can then be performed usingmultilateration based on the signal strength to nearby antenna masts.Further, many cellular phones include a GPS unit, which allows the phoneto determine and send its location based on satellites. Users of thephones 54A can opt-in to being tracked by downloading an applicationthat periodically reports the position of the phone to the analytics 68.The application may also send a unique identification code so thatphones can be distinguished from one another.

The transit analytics 68 can compare its knowledge of existing transitroutes with the received locations to determine whether a locationcorresponds to a rider or a non-rider. For example, if several locationsfor the same phone are similar to points along an existing bus route, itcan be assumed that this phone and the locations its reports correspondto a rider. However, if most or all of the positions reported from asame phone differ from that of all transit routes, it can be assumedthat this phone and locations it reports belong to a non-rider. Further,while an automobile driver may coincidentally follow some of the samepoints as a known bus route; it is unlikely that they will take themall. Moreover, the transit analytics 68 can examine the timestamps ofthe locations received by the phones to determine an average speed ofthe phone. If the average speed of the phone exceeds that which isexpected of public transit, it can be assumed that the phone and thelocations its reports belong to a non-rider.

Automobile computer systems 54N may report their positions to thetransit analytics 68 in a manner similar to the phones 54A. However,since these positions are coming from automobiles, it can be assumedthat they come from non-riders.

The positions of riders can also be determined by placing RadioFrequency Identification (RFID) systems 54F having RFID or Near FieldCommunication (NFC) sensors in transit terminals, transit entryways,transit exits, transit vehicles, etc. The rider can carry a fare cardwith an embedded RFID transmitter or a NFC transmitter. When the riderbrings the fare card near an RFID system 54F, the system can communicatethe position of a rider to the transit analytics 68. For example, atransportation vehicle can determine its own position using a built-inGPS or other methods, and report that as the rider's position when itsRFID sensor is triggered by the fare card. The fare cards may transmit aunique identification to the RFID sensor so that it can be determinedwhether a rider has left the transportation vehicle. For example, when arider enters and leaves a transportation vehicle with a fare card, theRFID sensor receives their ID. Since the entry and exit are likely to bespaced apart, the first reception of the ID could be interpreted as therider getting on, while the second reception could be interpreted as therider exiting.

The positions of non-riders may also be sent to the analytics 68 usingRFID systems 54F. For example, some toll booths are equipped with anRFID system that automatically charges an automobile driver a toll whenthey receive an RFID transmission from a tag on the automobile. Thesetoll booths can be altered to send their position to the transitanalytics 68 as a position of a non-rider each time they charge anautomobile a toll. Further, additional such RFID systems 54 f could beplaced throughout a city to send additional positions of the non-ridersto the transit analytics 68. While transit vehicles such as buses mayalso pass through such RFID systems 54, the RFID message sent from eachvehicle may be configured to identify the vehicle type to preventpositions of transit vehicles from being erroneously listed asnon-riders.

The transit routes may also be deduced by the transit analytics 68 byreceipt of location information from transit vehicles. The transitvehicles may transmit an identification code, their transit type (e.g.,bus, subway), and their position to the transit analytics 68 each timethey stop at a new stop so the transit analytics 68 can deduce the routefollowed and store it for later use.

Once the locations of the riders and the locations of the transit routesare known, a rider transit demand graph can be generated. For example,as discussed above, it is possible to determine the number of passengersat each stop and the positions of each stop relative to an existingroute. Thus, this number can be applied to a corresponding leg of theroute as the rider demand for that leg. The process can be repeated forall legs of the route until a complete rider transit demand graph hasbeen generated for the route, which corresponds to block (S501), whichcan collect one or more rider transit demand graphs.

The collection of the non-rider conditional information at block S502may be performed before or after the rider transit demand graphs arecollected. The method of block S505, collecting the rider conditionalinformation, is optional and may be performed independently of thecollection of the non-rider conditional information. As discussed above,the non-rider conditional information includes conditions that ifsatisfied, would cause a non-rider (e.g., an auto driver) to beconverted into a rider (e.g., a bus rider) and the rider conditionalinformation includes conditions that if satisfied would cause the rider(e.g., subway rider) to be converted into a non-rider (e.g., autodriver).

For example, the conditional information can be derived from feedbackinput by non-riders and riders through surveys. For example, non-riderscan be asked to list features that would get them to take publictransportation and riders can be asked to list features that would getthem to stop taking public transportation. Since non-riders and ridersalike may not know what features to list, the surveys may provide a listof parameters that can be ranked according to how likely it is to getthem to take public transportation or stop taking public transportation.

The below provides some examples of parameters and survey questions.However, embodiments of the present invention are not limited thereto,as various parameters and survey questions may be provided.

Examples of the parameters may include transit vehicle stop distancesfrom certain locations/landmarks (home, business, grocery, library,etc.), desired wait times, the presence/absence of certain features, forexample, air conditioning, reclining seats, padded seats, music, WIFI,television, overhead storage, automated announcements, a lift for awheelchair, bicycle carriers, a particular vehicle fuel type (naturalgas) etc.

As an example, a survey could ask the non-rider whether the presence ofa transit vehicle stop within a thousand feet of a certain location islikely to make them take public transit, while the rider could be askedwhether moving a transit vehicle stop they currently use an additional1000 feet away is likely to make them stop taking public transit.

If the survey provides several different stop distances (e.g., 1000feet, 2000 feet, 1 mile, etc.) between each location and the proposedtransit stop, the person could rank each with a numerical valueindicating how much more or less likely it would get them to take orstop taking public transit. For example, assume a non-rider ranksplacing a transit vehicle stop within 1000 feet of his home a 9 (e.g.,very likely to get him to take public transit), but ranks placing atransit vehicle stop within a mile of his home as a 4 (e.g., veryunlikely to get him to take public transit). Percentages could then beapplied to each numeral value, where a 9 could mean that a stop within1000 feet has a 90% chance making this person take public transit, whileplacing the stop within a mile only has a 40% chance of getting thisperson to take public transit.

As another example, the survey could ask a non-rider whether an averagewait time of 20 minutes for a transit vehicle is likely to get them totake public transit, or ask a rider whether increasing the current waittime by an additional 20 minutes is likely to get them to stop takingpublic transportation. As with the stop distances, the individuals maybe asked to rank several wait times using numeral values.

As another example, a survey may query individuals on combinations ofparameters. For example, a non-rider may be asked whether a bus stopwithin 2000 feet of their home and with an average waiting time of 25minutes is likely or unlikely to get them to take the bus.

As another example, a survey may provide a list of parameters, where thenon-rider/rider selects the minimum set of parameters that would getthem to take public transit or stop taking public transit.

The surveys may be administered to the non-riders and riders in variousways, such as in person interviews, over the phone, through the mail, orelectronically via email, social network mechanisms, instant message,text messages, mobile applications, desktop applications, etc. Theresults of the surveys sent electronically can be sent to the transitanalytics 68 over the internet and stored in a database in the cloud 50.The results of the non-electronic surveys can be entered manually to thetransit analytics 68.

The conditional information about what could get a rider to become anon-rider and a rider to become a non-rider can be derived from dataother than surveys, such as by analyzing current transit patterns (e.g.,from road sensors 54E) and positions of mobile phone/pda 54A (e.g., fromGPS positioning, GSM positioning, etc.). For example, if current trafficpatterns suggest a major delay or a complete blockage of a major roadwayartery, it is more likely that riders will be converted into non-riders.Further, traffic congestion or a complete blockage could be inferredwhen a multitude of phone/pda 54A positions are on known roadways, butare moving much more slowly than the average expected speed. Further, ifa new roadway has opened up, and traffic congestion has been severelyreduced as compared to a previous measure, it may be more likely thatriders will become converted into non-riders.

As discussed above with reference to block S503, the non-rider transitdemand graph is generated by satisfying a condition of the non-riderconditional information. For example, a user of the transit analytics 68can arbitrarily satisfy any one of the derived conditions. For example,assume a user has satisfied the condition that 50% of non-riders will bebecome riders if stops were located within 1000 feet of a mall and a1000 feet of a grocery store they routinely visit. The known locationsof non-riders can then be compared against the location of the mall andthe grocery to determine how many non-riders routinely visit both themall and the grocery store. If it is assumed that a transit routealready exists with a bus stop to the mall and a new stop is added tothe grocery store, it can be expected that the non-rider demand for thatleg of the route will become 10 if 20 non-riders were discoveredroutinely visiting both the mall and the grocery. Thus, the non-ridertransit demand graph could be illustrated as a leg of the known routelabeled with a non-rider demand of 10.

However, assume that the addition of the new stop causes an additionalwait time of 10 minutes to a subsequent stop. Further, assume that therider transit demand graph initially collected from block S501 indicatedthat a demand of 50 riders for that route and that the rider conditionalinformation collected in block S505 indicated that 10% of riders willbecome non-riders if their wait time is increased by 10 minutes. Thus,at block S506 an updated demand transit graph may be generated for theroute having a rider demand of 45. At block S507, a normalized transitdemand graph may be generated having a demand of 55 (e.g., 10 fromnon-riders and 45 from riders). In this example, since the ridership hasbeen increased (e.g., from 50 to 55), a city official could use thisinformation as an incentive to add the new stop to the route. Thus,before actually applying a change to a transit route, a user of thetransit analytics 68 can predict what effect this change is likely tohave on the current ridership of that route from both existing ridersand those that might become riders as a result of the change.

It is to be understood that exemplary embodiments disclosed above areillustrative only, as the invention may be modified and practiced indifferent but equivalent manners apparent to those skilled in the arthaving the benefit of the teachings herein. It is therefore evident thatexemplary embodiments disclosed herein may be altered or modified andall such variations are considered within the scope and spirit of theinvention.

1. A method of estimating a transit demand graph, the method comprising:collecting conditional information that includes at least one conditionthat when satisfied converts at least one non-rider into a rider,wherein riders use public transit and non-riders do not use publictransit; generating, by a processor, a non-rider transit demand graph bysatisfying one of the conditions, wherein the non-rider transit demandgraph shows the demand of the non-riders for a public transit route; andgenerating a normalized transit demand graph from the non-rider transitdemand graph and a rider transit demand graph, wherein the rider transitdemand graph shows the demand of riders for the same public transitroute.
 2. The method of claim 1, wherein collecting the conditionalinformation comprises: parsing survey results for feedback on changesproposed by a survey that would encourage a non-rider to become a rider;and deriving the conditions from the feedback.
 3. The method of claim 1,wherein generating the non-rider transit demand graph comprises:determining non-riders that are within a predetermined distance of thepublic transit route; determining a subset of the non-riders that becomeriders when the condition is satisfied; and generating the non-ridertransit demand graph from the sub-set of non-riders and the publictransit route.
 4. The method of claim 3, wherein determining thenon-riders comprises: comparing a current position of a mobile phoneagainst a subsequent position of the same mobile phone to determine aspeed; and determining a non-rider when the speed is outside a rangeexpected for a transit vehicle.
 5. The method of claim 1, wherein beforethe normalized transit demand graph is generated, the method comprises:collecting rider conditional information that includes at least onerider condition that when satisfied converts at least one rider into anon-rider; and updating the rider transit demand graph by satisfying oneof the rider conditions.
 6. The method of claim 5, wherein collectingthe rider conditional information comprises: parsing survey results forfeedback on changes proposed by a survey that would encourage a rider tobecome a non-rider; and deriving the rider conditions from the feedback.7. The method of claim 1, wherein the rider transit demand graph isgenerated by: determining riders that are within a predetermineddistance of the public transit route; and generating the rider transitdemand graph from the determined riders and the public transit route. 8.The method of claim 7, wherein determining the riders comprises:comparing positions of a mobile phone against waypoints of the route;and determining a rider when the positions of the mobile phonecorrespond with those of the waypoints.
 9. The method of claim 8,wherein the positions correspond when a majority of the positions of themobile phone are considered to be inside a transit vehicle.
 10. Themethod of claim 1, wherein the rider transit demand graph is generatedbased on a tally of a number of fare cards used on a transit vehiclefollowing the route.
 11. A method of estimating a transit demand graph,the method comprising: generating, by a processor, a non-rider transitdemand graph from positions of non-riders, positions of waypoints ofpublic transit routes, and at least one condition that directs at leastone of the non-riders to become a rider of a public transit route,wherein the non-rider transit demand graph shows the demand ofnon-riders for the public transit route; and generating a transit demandgraph from the non-rider transit demand graph and a rider transit demandgraph, wherein the rider transit demand graph shows the demand of ridersfor the same public transit route.
 12. The method of claim 11, whereinthe rider transit demand graph is generated from positions of riders ofthe route and waypoints of the route.
 13. The method of claim 12,wherein the rider transit demand graph is additionally generated basedon at least one rider condition that directs at least one of the ridersto become a non-rider of the route.
 14. The method of claim 12, whereinthe positions of the non-riders are determined from mobile phonepositions, and the mobile phone positions that do not correlate with theroute are the positions of the non-riders.
 15. The method of claim 12,wherein the positions of the riders are determined from mobile phonepositions, and the mobile phone positions that correlate with the routeare the rider positions.
 16. The method of claim 12, wherein acorresponding one of the conditions indicate a percentage of thenon-riders near the route will become riders, and the non-rider transitdemand graph is created from that percentage of the non-rider positionsnear the route.
 17. The method of claim 13, wherein a corresponding oneof the rider conditions indicate a percentage of the riders of the routewill become non-riders, and the rider transit demand graph is createdfrom the percentage of the rider positions of the route.
 18. A computerprogram product for estimating a transit demand graph, the computerprogram product comprising: a computer readable storage medium havingcomputer readable program code embodied therewith, the computer readableprogram, code comprising: computer readable program code configured togenerate a non-rider transit demand graph from positions of non-riders,positions of waypoints of public transit routes, and at least onecondition that directs at least one of the non-riders to become a riderof a public transit route, and generate a transit demand graph from thenon-rider transit demand graph and a rider transit demand graph.
 19. Thecomputer program product of claim 18, wherein the non-rider transitdemand graph shows the demand of non-riders for the public transit routeand the rider transit demand graph shows the demand of riders for thesame public transit route.
 20. The computer program product of claim 19,wherein the positions of the non-riders are determined from mobile phonepositions that do not correlate with any of the public transit routes,and wherein the positions of the riders are determined from the mobilephone positions that correlate with one of the public transit routes.